Backpropagation Convergence Via Deterministic Nonmonotone Perturbed Minimization

The fundamental backpropagation (BP) algorithm for training artificial neuralnetworks is cast as a deterministic nonmonotone perturbed gradientmethod. Under certain natural assumptions, such as the series of learning rates diverging while the series of their squares converging, it is established that every accumulation point of the online BP iterates is a stationary point of the BP error function. Theresults presented cover serial and parallel online BP, modified BP with a momentum term, and BP with weight decay. 1 INTRODUCTION